From Interatomic Distances to Protein Tertiary Structures with a Deep Convolutional Neural Network

Elucidating biologically-active protein structures remains a daunting task both in the wet and dry laboratory, and many proteins lack structural characterization. This lack of knowledge continues to motivate the development of computational methods for protein structure prediction. Methods are diverse in their approaches, and recent efforts have debuted deep learning-based methods for various sub-problems within the larger problem of protein structure prediction. In this paper, we focus on such a sub-problem, the reconstruction of three-dimensional structures consistent with given inter-atomic distances. Inspired by a recent architecture put forward in the larger context of generative frameworks, we design and evaluate a deep convolutional network model on experimentally- and computationally-obtained tertiary structures. Comparison with convex and stochastic optimization-based methods shows that the deep model is faster and similarly or more accurate, opening up several venues of further research to advance the larger problem of protein structure prediction.